The social gradient in Subjective Well-being (SWB)
Bibliographic record
Abstract
Background: The Social gradient in Subjective wellbeing (SWB) exists in countries, and in individuals, either rich or poor, and the pattern can be seen when looking at the factors of Socio-economic Position (SEP) is a strong predictor of SWB and as well as a popular concept in health and happiness research. \n\nAim: The main aim of this paper is firstly, to determine whether there are socio-economic gradients in SWB, and secondly, to evaluate how are different socioeconomic variables are associated with different measures of SWB. \n\nMethod: This is a cross-sectional study that uses data from a survey titled "People's Views on Socioeconomic Position" that was conducted in three countries: the UK, the US, and Canada. The main analysis was conducted by means of multiple linear regression analysis, which was used to investigate the association of SEP with SWB, measured by four different SWB outcome variables: Global life Satisfaction (GLS), Personal wellbeing index (PWI), Job satisfaction, and Meaningfulness. Education, household income, relative income, Childhood financial circumstances (CFC), father´s education, mother´s education, and being born native along with demographic variables (age, sex, marital status, and country ) are the independent variable. \n\nResults: The four measures of SWB were significantly impacted by SEP. The relationship between the four SWB measures with education, relative income, and childhood financial circumstances all showed statistically significant associations. This indicates that higher education, relative income, and CFC influenced SWB positively. Marital status was significantly and positively associated with SWB. The additional thing to note is, when relative income is considered, the magnitude of the link between absolute income and SWB broadly disappeared and turns insignificant. \n \nConclusion: This study reported indicates an existence of a social gradient in SWB. It was noticed that education, relative income, CFC and marital status have the greatest influence on SWB. Lower levels of education, low relative income, poor childhood financial circumstances, and being single predict lower SWB. \n Keywords: Social Gradient, Subjective Well-being, Socioeconomic Position, Personal wellbeing Index, Global Life satisfaction, Job Satisfaction, Meaningfulness
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".